Overview

Dataset statistics

Number of variables12
Number of observations243
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.9 KiB
Average record size in memory96.5 B

Variable types

Numeric10
Categorical2

Alerts

BUI is highly overall correlated with Classes and 7 other fieldsHigh correlation
Classes is highly overall correlated with BUI and 7 other fieldsHigh correlation
DC is highly overall correlated with BUI and 7 other fieldsHigh correlation
DMC is highly overall correlated with BUI and 8 other fieldsHigh correlation
FFMC is highly overall correlated with BUI and 8 other fieldsHigh correlation
FWI is highly overall correlated with BUI and 8 other fieldsHigh correlation
ISI is highly overall correlated with BUI and 8 other fieldsHigh correlation
RH is highly overall correlated with DMC and 4 other fieldsHigh correlation
Rain is highly overall correlated with BUI and 6 other fieldsHigh correlation
Temperature is highly overall correlated with BUI and 7 other fieldsHigh correlation
Rain has 133 (54.7%) zerosZeros
ISI has 4 (1.6%) zerosZeros
FWI has 9 (3.7%) zerosZeros

Reproduction

Analysis started2024-03-20 16:43:13.782120
Analysis finished2024-03-20 16:43:27.427588
Duration13.65 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Temperature
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.152263
Minimum22
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-20T16:43:27.511148image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile26
Q130
median32
Q335
95-th percentile37.9
Maximum42
Range20
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6280395
Coefficient of variation (CV)0.11283932
Kurtosis-0.14141446
Mean32.152263
Median Absolute Deviation (MAD)3
Skewness-0.19132733
Sum7813
Variance13.16267
MonotonicityNot monotonic
2024-03-20T16:43:27.666407image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
35 29
11.9%
31 25
10.3%
34 24
9.9%
33 23
9.5%
30 22
9.1%
36 21
8.6%
32 21
8.6%
29 18
7.4%
28 15
6.2%
37 8
 
3.3%
Other values (9) 37
15.2%
ValueCountFrequency (%)
22 2
 
0.8%
24 3
 
1.2%
25 6
 
2.5%
26 5
 
2.1%
27 8
 
3.3%
28 15
6.2%
29 18
7.4%
30 22
9.1%
31 25
10.3%
32 21
8.6%
ValueCountFrequency (%)
42 1
 
0.4%
40 3
 
1.2%
39 6
 
2.5%
38 3
 
1.2%
37 8
 
3.3%
36 21
8.6%
35 29
11.9%
34 24
9.9%
33 23
9.5%
32 21
8.6%

RH
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.041152
Minimum21
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-20T16:43:27.850776image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile37
Q152.5
median63
Q373.5
95-th percentile86
Maximum90
Range69
Interquartile range (IQR)21

Descriptive statistics

Standard deviation14.82816
Coefficient of variation (CV)0.23900523
Kurtosis-0.50894281
Mean62.041152
Median Absolute Deviation (MAD)11
Skewness-0.24279046
Sum15076
Variance219.87433
MonotonicityNot monotonic
2024-03-20T16:43:28.055654image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55 10
 
4.1%
64 10
 
4.1%
78 8
 
3.3%
54 8
 
3.3%
58 8
 
3.3%
73 7
 
2.9%
80 7
 
2.9%
66 7
 
2.9%
65 7
 
2.9%
68 7
 
2.9%
Other values (52) 164
67.5%
ValueCountFrequency (%)
21 1
 
0.4%
24 1
 
0.4%
26 1
 
0.4%
29 1
 
0.4%
31 1
 
0.4%
33 2
0.8%
34 3
1.2%
35 1
 
0.4%
36 1
 
0.4%
37 3
1.2%
ValueCountFrequency (%)
90 1
 
0.4%
89 3
1.2%
88 3
1.2%
87 4
1.6%
86 3
1.2%
84 2
 
0.8%
83 1
 
0.4%
82 3
1.2%
81 6
2.5%
80 7
2.9%

Ws
Real number (ℝ)

Distinct18
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.493827
Minimum6
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-20T16:43:28.229102image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q114
median15
Q317
95-th percentile20
Maximum29
Range23
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8113853
Coefficient of variation (CV)0.18145196
Kurtosis2.6217035
Mean15.493827
Median Absolute Deviation (MAD)2
Skewness0.55558584
Sum3765
Variance7.9038874
MonotonicityNot monotonic
2024-03-20T16:43:28.378870image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
14 43
17.7%
15 40
16.5%
13 30
12.3%
17 28
11.5%
16 27
11.1%
18 25
10.3%
19 15
 
6.2%
21 8
 
3.3%
12 7
 
2.9%
11 7
 
2.9%
Other values (8) 13
 
5.3%
ValueCountFrequency (%)
6 1
 
0.4%
8 1
 
0.4%
9 2
 
0.8%
10 3
 
1.2%
11 7
 
2.9%
12 7
 
2.9%
13 30
12.3%
14 43
17.7%
15 40
16.5%
16 27
11.1%
ValueCountFrequency (%)
29 1
 
0.4%
26 1
 
0.4%
22 2
 
0.8%
21 8
 
3.3%
20 2
 
0.8%
19 15
 
6.2%
18 25
10.3%
17 28
11.5%
16 27
11.1%
15 40
16.5%

Rain
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.76296296
Minimum0
Maximum16.8
Zeros133
Zeros (%)54.7%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-20T16:43:28.542916image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.5
95-th percentile4.37
Maximum16.8
Range16.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation2.0032068
Coefficient of variation (CV)2.6255623
Kurtosis25.822987
Mean0.76296296
Median Absolute Deviation (MAD)0
Skewness4.5686298
Sum185.4
Variance4.0128375
MonotonicityNot monotonic
2024-03-20T16:43:28.730425image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 133
54.7%
0.1 18
 
7.4%
0.2 11
 
4.5%
0.3 10
 
4.1%
0.4 8
 
3.3%
0.6 6
 
2.5%
0.7 6
 
2.5%
0.5 5
 
2.1%
1.2 3
 
1.2%
1.1 3
 
1.2%
Other values (29) 40
 
16.5%
ValueCountFrequency (%)
0 133
54.7%
0.1 18
 
7.4%
0.2 11
 
4.5%
0.3 10
 
4.1%
0.4 8
 
3.3%
0.5 5
 
2.1%
0.6 6
 
2.5%
0.7 6
 
2.5%
0.8 2
 
0.8%
0.9 1
 
0.4%
ValueCountFrequency (%)
16.8 1
0.4%
13.1 1
0.4%
10.1 1
0.4%
8.7 1
0.4%
8.3 1
0.4%
7.2 1
0.4%
6.5 1
0.4%
6 1
0.4%
5.8 1
0.4%
4.7 1
0.4%

FFMC
Real number (ℝ)

HIGH CORRELATION 

Distinct173
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.842387
Minimum28.6
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-20T16:43:28.927816image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum28.6
5-th percentile47.13
Q171.85
median83.3
Q388.3
95-th percentile92.19
Maximum96
Range67.4
Interquartile range (IQR)16.45

Descriptive statistics

Standard deviation14.349641
Coefficient of variation (CV)0.18434226
Kurtosis1.040087
Mean77.842387
Median Absolute Deviation (MAD)5.8
Skewness-1.3201301
Sum18915.7
Variance205.9122
MonotonicityNot monotonic
2024-03-20T16:43:29.120926image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.9 7
 
2.9%
89.4 5
 
2.1%
89.1 4
 
1.6%
85.4 4
 
1.6%
89.3 4
 
1.6%
88.3 3
 
1.2%
78.3 3
 
1.2%
87 3
 
1.2%
88.1 3
 
1.2%
47.4 3
 
1.2%
Other values (163) 204
84.0%
ValueCountFrequency (%)
28.6 1
0.4%
30.5 1
0.4%
36.1 1
0.4%
37.3 1
0.4%
37.9 1
0.4%
40.9 1
0.4%
41.1 1
0.4%
42.6 1
0.4%
44.9 1
0.4%
45 1
0.4%
ValueCountFrequency (%)
96 1
0.4%
94.3 1
0.4%
94.2 1
0.4%
93.9 2
0.8%
93.8 1
0.4%
93.7 1
0.4%
93.3 1
0.4%
93 1
0.4%
92.5 2
0.8%
92.2 2
0.8%

DMC
Real number (ℝ)

HIGH CORRELATION 

Distinct165
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.680658
Minimum0.7
Maximum65.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-20T16:43:29.303150image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile1.9
Q15.8
median11.3
Q320.8
95-th percentile41.04
Maximum65.9
Range65.2
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.39304
Coefficient of variation (CV)0.84417465
Kurtosis2.462551
Mean14.680658
Median Absolute Deviation (MAD)6.9
Skewness1.5229829
Sum3567.4
Variance153.58743
MonotonicityNot monotonic
2024-03-20T16:43:29.489940image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.9 5
 
2.1%
1.9 4
 
1.6%
12.5 4
 
1.6%
16 3
 
1.2%
7 3
 
1.2%
2.5 3
 
1.2%
9.7 3
 
1.2%
3.2 3
 
1.2%
1.3 3
 
1.2%
2.6 3
 
1.2%
Other values (155) 209
86.0%
ValueCountFrequency (%)
0.7 1
 
0.4%
0.9 2
0.8%
1.1 2
0.8%
1.2 1
 
0.4%
1.3 3
1.2%
1.7 1
 
0.4%
1.9 4
1.6%
2.1 1
 
0.4%
2.2 2
0.8%
2.4 1
 
0.4%
ValueCountFrequency (%)
65.9 1
0.4%
61.3 1
0.4%
56.3 1
0.4%
54.2 1
0.4%
51.3 1
0.4%
50.2 1
0.4%
47 1
0.4%
46.6 1
0.4%
46.1 1
0.4%
45.6 1
0.4%

DC
Real number (ℝ)

HIGH CORRELATION 

Distinct197
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.430864
Minimum6.9
Maximum220.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-20T16:43:29.685921image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum6.9
5-th percentile7.6
Q112.35
median33.1
Q369.1
95-th percentile158.94
Maximum220.4
Range213.5
Interquartile range (IQR)56.75

Descriptive statistics

Standard deviation47.665606
Coefficient of variation (CV)0.96428834
Kurtosis1.5964668
Mean49.430864
Median Absolute Deviation (MAD)23.9
Skewness1.4734602
Sum12011.7
Variance2272.01
MonotonicityNot monotonic
2024-03-20T16:43:29.880042image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 5
 
2.1%
8.4 4
 
1.6%
7.8 4
 
1.6%
7.5 4
 
1.6%
8.3 4
 
1.6%
8.2 4
 
1.6%
7.6 4
 
1.6%
17 3
 
1.2%
15.2 2
 
0.8%
10 2
 
0.8%
Other values (187) 207
85.2%
ValueCountFrequency (%)
6.9 1
 
0.4%
7 2
0.8%
7.1 1
 
0.4%
7.3 2
0.8%
7.4 2
0.8%
7.5 4
1.6%
7.6 4
1.6%
7.7 2
0.8%
7.8 4
1.6%
7.9 1
 
0.4%
ValueCountFrequency (%)
220.4 1
0.4%
210.4 1
0.4%
200.2 1
0.4%
190.6 1
0.4%
181.3 1
0.4%
180.4 1
0.4%
177.3 1
0.4%
171.3 1
0.4%
168.2 1
0.4%
167.2 1
0.4%

ISI
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct106
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7423868
Minimum0
Maximum19
Zeros4
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-20T16:43:30.064888image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q11.4
median3.5
Q37.25
95-th percentile13.38
Maximum19
Range19
Interquartile range (IQR)5.85

Descriptive statistics

Standard deviation4.1542338
Coefficient of variation (CV)0.87597954
Kurtosis0.86232522
Mean4.7423868
Median Absolute Deviation (MAD)2.4
Skewness1.1402426
Sum1152.4
Variance17.257659
MonotonicityNot monotonic
2024-03-20T16:43:30.267532image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1 8
 
3.3%
1.2 7
 
2.9%
0.4 5
 
2.1%
5.2 5
 
2.1%
4.7 5
 
2.1%
2.8 5
 
2.1%
1 5
 
2.1%
1.5 5
 
2.1%
5.6 5
 
2.1%
0.1 4
 
1.6%
Other values (96) 189
77.8%
ValueCountFrequency (%)
0 4
1.6%
0.1 4
1.6%
0.2 4
1.6%
0.3 3
1.2%
0.4 5
2.1%
0.5 2
 
0.8%
0.6 4
1.6%
0.7 4
1.6%
0.8 3
1.2%
0.9 2
 
0.8%
ValueCountFrequency (%)
19 1
0.4%
18.5 1
0.4%
17.2 1
0.4%
16.6 1
0.4%
16 1
0.4%
15.7 2
0.8%
15.5 1
0.4%
14.3 1
0.4%
14.2 1
0.4%
13.8 2
0.8%

BUI
Real number (ℝ)

HIGH CORRELATION 

Distinct173
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.690535
Minimum1.1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-20T16:43:30.467850image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile2.42
Q16
median12.4
Q322.65
95-th percentile46.4
Maximum68
Range66.9
Interquartile range (IQR)16.65

Descriptive statistics

Standard deviation14.228421
Coefficient of variation (CV)0.85248443
Kurtosis1.9560166
Mean16.690535
Median Absolute Deviation (MAD)7.3
Skewness1.4527448
Sum4055.8
Variance202.44797
MonotonicityNot monotonic
2024-03-20T16:43:30.654482image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 5
 
2.1%
5.1 4
 
1.6%
3.9 3
 
1.2%
2.4 3
 
1.2%
8.3 3
 
1.2%
4.4 3
 
1.2%
2.9 3
 
1.2%
22.4 3
 
1.2%
11.5 3
 
1.2%
14.1 3
 
1.2%
Other values (163) 210
86.4%
ValueCountFrequency (%)
1.1 1
 
0.4%
1.4 2
0.8%
1.6 2
0.8%
1.7 2
0.8%
1.8 2
0.8%
2.2 1
 
0.4%
2.4 3
1.2%
2.6 2
0.8%
2.7 2
0.8%
2.8 2
0.8%
ValueCountFrequency (%)
68 1
0.4%
67.4 1
0.4%
64 1
0.4%
62.9 1
0.4%
59.5 1
0.4%
59.3 1
0.4%
57.1 1
0.4%
54.9 1
0.4%
54.7 1
0.4%
50.9 1
0.4%

FWI
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct125
Distinct (%)51.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0353909
Minimum0
Maximum31.1
Zeros9
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-20T16:43:30.845322image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.7
median4.2
Q311.45
95-th percentile21.53
Maximum31.1
Range31.1
Interquartile range (IQR)10.75

Descriptive statistics

Standard deviation7.4405677
Coefficient of variation (CV)1.0575912
Kurtosis0.65498526
Mean7.0353909
Median Absolute Deviation (MAD)3.8
Skewness1.1475925
Sum1709.6
Variance55.362048
MonotonicityNot monotonic
2024-03-20T16:43:31.038437image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4 12
 
4.9%
0.8 10
 
4.1%
0.5 9
 
3.7%
0.1 9
 
3.7%
0 9
 
3.7%
0.3 8
 
3.3%
0.9 7
 
2.9%
0.2 6
 
2.5%
0.7 5
 
2.1%
0.6 4
 
1.6%
Other values (115) 164
67.5%
ValueCountFrequency (%)
0 9
3.7%
0.1 9
3.7%
0.2 6
2.5%
0.3 8
3.3%
0.4 12
4.9%
0.5 9
3.7%
0.6 4
 
1.6%
0.7 5
2.1%
0.8 10
4.1%
0.9 7
2.9%
ValueCountFrequency (%)
31.1 1
0.4%
30.3 1
0.4%
30.2 1
0.4%
30 1
0.4%
26.9 1
0.4%
26.3 1
0.4%
26.1 1
0.4%
25.4 1
0.4%
24.5 1
0.4%
24 1
0.4%

Classes
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
1
137 
0
106 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters243
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 137
56.4%
0 106
43.6%

Length

2024-03-20T16:43:31.556188image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-20T16:43:31.705094image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 137
56.4%
0 106
43.6%

Most occurring characters

ValueCountFrequency (%)
1 137
56.4%
0 106
43.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 243
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 137
56.4%
0 106
43.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 243
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 137
56.4%
0 106
43.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 243
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 137
56.4%
0 106
43.6%

Region
Categorical

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
163 
1
80 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters243
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 163
67.1%
1 80
32.9%

Length

2024-03-20T16:43:31.858946image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-20T16:43:31.984644image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 163
67.1%
1 80
32.9%

Most occurring characters

ValueCountFrequency (%)
0 163
67.1%
1 80
32.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 243
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 163
67.1%
1 80
32.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 243
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 163
67.1%
1 80
32.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 243
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 163
67.1%
1 80
32.9%

Interactions

2024-03-20T16:43:25.831731image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:14.054863image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:15.344924image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:16.561685image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:17.909591image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:19.173709image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:20.441346image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:21.872926image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:23.040122image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:24.328867image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:25.966418image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:14.197237image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:15.471024image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:16.684149image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:18.043162image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:19.307812image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:20.598237image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:22.001633image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:23.183063image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:24.459077image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:26.087444image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:14.336801image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:15.588275image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:16.996628image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:18.167389image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:19.439415image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:20.938097image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:22.121489image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:23.314882image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:24.582793image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:26.207777image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:14.456102image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:15.697136image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:17.108510image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:18.287308image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:19.560463image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:21.045573image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:22.228637image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:23.445479image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:24.711850image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:26.332139image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:14.586620image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:15.823841image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:17.234650image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:18.405558image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:19.690513image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:21.165098image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:22.353071image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:23.574315image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:24.837679image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:26.457344image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:14.710557image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:15.934856image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:17.352581image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:18.528972image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:19.809998image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:21.278836image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:22.465503image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:23.713048image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:24.959487image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:26.574157image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:14.841789image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:16.056542image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:17.463875image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:18.660973image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:19.930515image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:21.385268image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:22.578552image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:23.842518image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:25.080285image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:26.681066image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:14.969546image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:16.177970image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:17.558678image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:18.774100image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:20.044559image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:21.491943image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:22.686548image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:23.962476image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:25.193965image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:26.798209image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:15.105056image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:16.313240image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:17.676018image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:18.904687image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:20.197714image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:21.616735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:22.806477image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:24.087499image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:25.318328image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:26.917977image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:15.227986image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:16.444735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:17.798863image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:19.039284image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:20.318597image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:21.752073image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:22.924370image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:24.212132image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-20T16:43:25.716628image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-03-20T16:43:32.114875image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
BUIClassesDCDMCFFMCFWIISIRHRainRegionTemperatureWs
BUI1.0000.7260.9430.9880.8070.9110.811-0.467-0.5760.0000.5860.027
Classes0.7261.0000.6730.7170.8560.8420.857-0.421-0.6830.0500.521-0.023
DC0.9430.6731.0000.8930.7350.8490.746-0.347-0.6120.0770.5050.060
DMC0.9880.7170.8931.0000.8220.9160.822-0.505-0.5590.1340.6110.001
FFMC0.8070.8560.7350.8221.0000.9680.989-0.665-0.7410.0000.666-0.067
FWI0.9110.8420.8490.9160.9681.0000.975-0.598-0.7180.0870.6570.034
ISI0.8110.8570.7460.8220.9890.9751.000-0.643-0.7380.0000.6480.032
RH-0.467-0.421-0.347-0.505-0.665-0.598-0.6431.0000.1790.000-0.6430.201
Rain-0.576-0.683-0.612-0.559-0.741-0.718-0.7380.1791.0000.000-0.2930.011
Region0.0000.0500.0770.1340.0000.0870.0000.0000.0001.0000.059-0.003
Temperature0.5860.5210.5050.6110.6660.6570.648-0.643-0.2930.0591.000-0.224
Ws0.027-0.0230.0600.001-0.0670.0340.0320.2010.011-0.003-0.2241.000

Missing values

2024-03-20T16:43:27.092809image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-20T16:43:27.336286image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TemperatureRHWsRainFFMCDMCDCISIBUIFWIClassesRegion
02777160.064.83.014.21.23.90.500
13354130.088.29.930.56.410.97.210
23073150.086.612.138.35.613.57.110
32879120.073.29.546.31.312.60.900
43078200.559.04.67.81.04.40.400
52880173.149.43.07.40.43.00.100
63089160.637.31.17.80.01.60.000
73155160.179.94.516.02.55.31.400
83262180.181.48.247.73.311.53.810
93164180.086.817.871.86.721.610.610
TemperatureRHWsRainFFMCDMCDCISIBUIFWIClassesRegion
2333337160.092.261.3167.213.164.030.311
2343553170.580.220.7149.22.730.65.911
2352867190.075.42.916.32.04.00.801
2363066150.273.54.126.61.56.00.701
2373073140.079.26.516.62.16.61.201
2382881150.084.612.641.54.314.35.711
2393444120.092.525.263.311.226.217.511
2403458130.279.518.788.02.124.43.801
2412870150.079.913.836.12.414.13.001
2422787290.545.93.57.90.43.40.201